Next, we study how well our dense representations, once learned, generalize to other datasets.
We train our model on ImageNet-100 or COCO and report LC and overclustering results on PVOC12, COCO-Things and COCO-Stuff.
As shown in Table REF , we outperform self-supervised prior works by large margins on all three datasets even though some use further datasets and supervision.
On PVOC12 we surpass the state-of-the-art by more than \(17\%\)  for linear evaluation and by more than \(5\%\)  for overclustering.
On COCO-Things and COCO-Stuff we improve linear classifier by \(>5\%\)  and \(>3\%\)  and overclustering by \(>8\%\)  and \(>10\%\)  respectively.
Note that these gains are not due to the DINO initialisation nor due to ViTs per-se as the starting DINO model performs on par with other instance-level self-supervised methods that use ResNets like SwAV.
In fact, DINO's embedding space exhibits inferior semantic structure in comparison to MoCo-v2 and SwAV as can be seen from the overclustering results on PVOC12 (-18%) and COCO-Things (-12%).
Our method is also on par with the performance of a supervised ViT even though it was trained on a \(>\) 10x times larger full ImageNet (IN-21k) dataset [1]}.
When fine-tuning on COCO instead of IN-100, we see further improvements on all datasets.
The results confirm that it is desirable to learn object parts representations, as they work well under different object definitions, as evidenced by strong performances across datasets.
